Methods and apparatus for narrowband ranging systems using coarse and fine delay estimation

ABSTRACT

An example apparatus includes a transducer to receive a reference signal and a reflected signal, the reflected signal being the reference signal after being reflected of a target; a filter to generate a band-pass reference signal and a band-pass reflected signal by filtering (A) reference signal samples associated with the reference signal and (B) reflected signal samples associated with the reflected signal; a correlator to generate a first correlation by correlating the reference signal samples with the reflected signal samples and a second correlation by correlating the band-pass reference signal with the band-pass reflected signal; and a delay estimator to determine a distance to the target based on the first correlation (coarse delay) and the second correlation (fine delay) and output a signal including the distance to the target.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/208,948 filed Jul. 13, 2016, now U.S. Pat. No. 10,145,948, which isfully incorporated herein by reference.

BACKGROUND

This relates generally to ranging systems, and more particularly tomethods and apparatus for narrowband ranging systems using coarse andfine delay estimation.

A ranging system (e.g., RADAR, LIDAR, SONAR, etc.) determines a distanceto a target by transmitting a signal (e.g., radio signals, lightsignals, sound signals, etc.) at the target. The transmitted signal isreflected off the target and back to the ranging system. The rangingsystem compares received signals to the transmitted signal to determinewhen the reflected signal has been received. Such a ranging systemmeasures the distance to the target based on the amount of time betweenwhen the signal was transmitted and when the reflected signal wasreceived.

SUMMARY

In described examples, a distance to a target is determined using coarseand fine delay estimation based on a narrowband transmit signal. Anexample apparatus includes a transducer to receive a reference signaland a reflected signal, the reflected signal being the reference signalafter being reflected off a target. Such an apparatus further includes afilter to generate a band-pass reference signal and a band-passreflected signal by filtering (A) reference signal samples associatedwith the reference signal and (B) reflected signal samples associatedwith the reflected signal. Such an apparatus further includes acorrelator to generate a first correlation by correlating the band-passreference signal with the band-pass reflected signal and a secondcorrelation by correlating the reference signal samples with thereflected signal samples. Such an apparatus further includes a delayestimator to determine a distance to the target based on the firstcorrelation and the second correlation and output a signal including thedistance to the target.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an example ranging system that determines adistance to an example target.

FIG. 2 is a block diagram of an example distance estimator of FIG. 1.

FIG. 3 is an alternative block diagram of the example distance estimatorof FIG. 1.

FIG. 4 is a flowchart representative of example machine readableinstructions that may be executed to implement the example distanceestimator of FIG. 2 to determine the distance to the example target ofFIG. 1.

FIG. 5 illustrates graphs of an example reference signal transmitted bythe example transmission transducer of FIG. 1.

FIG. 6 illustrates graphs of an example windowed reflected signal and anexample windowed reference signal of FIG. 2.

FIG. 7 illustrates graphs of an example band-pass reflected signal andan example band-pass reference signal of FIG. 2.

FIGS. 8A and 8B are graphs illustrating example correlations of FIG. 2.

FIG. 9 is a block diagram of a processor platform structured to executethe example machine readable instructions of FIG. 4 to control theexample distance estimator of FIGS. 1 and 2.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

The drawings are not to scale. Wherever appropriate, the same referencenumbers are used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

Ranging systems used to determine distances to various objects havebecome increasingly popular with advances in robotic technologies,automotive technologies, etc. Ranging systems determine distances bytransmitting a signal to an object (e.g. a target). The transmittedsignal reflects off the object and returns to the ranging system. Theranging system determines when the reflected signal has been receivedand determines the distance of the target based on the duration of timebetween when the signal was transmitted and when the reflected signalwas received. Example ranging systems include radio detection andranging (RADAR) systems, light detection and ranging (LIDAR) systems,sound detection and ranging (SONAR) systems. Ranging systems may be usedto help robots interact with objects, transport to various locations,etc. Additionally, ranging systems may be used in automobiles to providesafety features and autonomous driving systems. Distance estimation fromsuch ranging systems requires high accuracy and precision to properlyfunction. As ranging systems become more popular, a low cost, lesscomplex, power saving implementation of a ranging system is highlydesirable.

Ranging systems are implemented by transmitting a signal to reflect offa target and return back to the ranging system. Such ranging systemscalculate the distance to the target based on the amount of time ittakes for the signal to reach the target, reflect off the target, andreturn to the ranging system (e.g., the time of flight of the reflectedsignal).

One conventional technique of implementing a ranging system includeslow-pass filtering a reflected signal and converting the reflectedlow-pass filtered signal into low-pass filtered reflected samples. Thelow-pass filtered reflected samples are compared (e.g., correlated) to alow-pass filtered transmitted signal samples (e.g., corresponding to thesignal prior to being reflected off the target) to determine when thereflected signal samples match the transmitted signal samples based on apeak intensity value (e.g., the highest intensity value) of thecorrelation. The peak corresponds to a time sample identifying the timeof flight of the transmitted signal after being reflected off a targetand returned to the ranging system (e.g., the reflected signal). Thetime of flight of the reflected signal corresponds to the distance ofthe target. However, correlating low-pass filtered reference signalsamples with low-pass filtered reflected signal samples to determine adistance of a target is accurate (e.g., has low ambiguity) but notprecise. Because the reflected signal is subject to noise, thecorrelation of the low-pass filtered reference signal samples and thelow-pass filtered reflected signal samples may include spikes thataffects (e.g., decreases the accuracy of) the estimation of thedistance.

Another conventional technique of implementing a ranging system includesband-pass filtering a received reflected signal and converting thereceived reflected band-pass filtered signal into band-pass filteredreflected samples. The band-pass filtered reflected samples are compared(e.g., correlated) to band-pass filtered transmitted reference signalsamples to determine when the received reflected signal samples matchthe transmitted reference signal samples based on a peak intensity valueof the correlation. The peak value corresponds to a time sampleidentifying the time of flight of the reflected signal. The time offlight of the reflected signal corresponds to the distance of thetarget. However, correlating band-pass filtered reference signals withband-pass filtered reflected signals to determine a distance of a targetis precise but not accurate. When two bandpass filtered signals arecorrelated, the correlation includes multiple local peaks outside of themain peak. The local peaks located closest to the main peak are hereinreferred to as side lobes and the main peak (e.g., highest intensityvalue of a correlation) is herein referred to as the peak. Because thereflected signal is subject to noise, the correlation of the band-passfiltered reflected signal samples and the band-pass filtered referencesignal samples may include spike on a side lobe causing the intensityvalue of the side lobe to increase to an intensity value higher than themain peak. Accidentally identifying a side lobe as a main peak due tonoise is herein referred to as a cycle slip. Such conventionaltechniques have a high probability of cycle slips leading to inaccuratedistance estimations. Examples disclosed herein provide a ranging systemwith the accuracy of a low-pass correlation and the precision of aband-pass correlation.

Examples disclosed herein include (A) applying a coarse correlationbetween low-pass filtered reference signal samples with low-passfiltered reflected signal samples to determine a coarse (e.g., accuratebut not precise) time delay estimate, (B) applying a fine correlationbetween band-pass filtered reference signal samples with band-passfiltered reflected signal samples to determine a fine (e.g., precise butnot accurate) time delay estimate, and (C) determining a distance to atarget based on combining the coarse time delay estimate and the finetime delay estimate. Using examples disclosed herein, both accuracy andprecision are optimized and the probability of cycle slip issubstantially reduced. Examples disclosed herein reduce range errorstandard deviation by 18%. Thus, examples disclosed herein allow aranging system to reduce signal transmission power by roughly 20%without affecting the range error standard deviation.

In some examples, low-pass narrowband reference signals and low-passnarrowband reflected signals are windowed prior to applying a coarsecorrelation to reduce the peak to side lobe ratio and lower theambiguity (e.g., increasing the accuracy and decreasing the probabilityof a cycle slip) associated with estimating a distance to a target.Windowing low-pass signals lowers the side lobes of the coarsecorrelation of the low-pass signals at the expense of further decreasingthe precision of the coarse correlation. However, since the coarsecorrelation is only used determine a coarse time delay and the finecorrelation is used to determine the optimal time delay based on thecoarse time delay, windowing may be used to further increase theaccuracy of a distance estimation.

As used herein, a “reference signal” is defined as the signaltransmitted by a transmission transducer after being received by areceiver transducer and “reflected signal” is defined as the transmittedsignal after being reflected off of the object and received by areceiver transducer. Although the reference signal and the reflectedsignal are based on the same transmitted signal, the reference signal ismeasured at transmission and the reflected signal is measured afterreflection and return. Thus, the reflected signal has to travel a muchfarther distance and may be subject to additional noise and may beoscillating with a different phase and/or may have a different amplitudethan the reference signal. As used herein, a time delay is hereindefined as the time of flight of a reflected signal. As used herein,low-pass filtered signals are used interchangeably with low-pass signalsand band-pass filtered signals are used interchangeably with band-passsignals.

The illustration of FIG. 1 illustrates an example ranging system 100 todetermine a distance to an example target 102. The example rangingsystem 100 includes an example transmission signal generator 104, anexample transmission transducer 106, an example transmitted signal 107,an example receiver reference signal transducer 108, an examplereflected signal 109, an example receiver reflected signal transducer110, an example reference signal path analog front end device (AFE) 112,an example reflected signal path AFE 114, and an example distanceestimator 116.

The example ranging system 100 of FIG. 1 is a system that transmitssignals 107 to the example target 102 and receives reflected signals 109(e.g., the transmitted signals 107 reflected off the example target 102)to determine a distance to the example target 102. The example rangingsystem 100 may be a RADAR system, a LIDAR system, a SONAR system, anultrasonic system, a hybrid system, and/or any other type of rangingsystem. As described above, a RADAR system utilizes radio signals (e.g.,electromagnetic waves), a LIDAR system utilizes light signals, and aSONAR system and/or ultrasonic system utilizes sound signals, and ahybrid system uses a light signal modulated with a radio signal.

The example transmission signal generator 104 of FIG. 1 generatessignals to be reflected off the example target 102. As described above,the signals may be light signals, sound signals, radio signals, hybridsignals, and/or any other type of signal. In some examples, thetransmission signal generator 104 generates a pulse, a sinusoid, asquare wave, a triangle wave, and/or any of type of signal. In theillustrated example of FIG. 1, because bandpass filtering narrowbandsignals maximizes signal to noise ratio (SNR) (e.g., leading toincreased precision), the example transmission signal generator 104generates narrowband signals. Alternatively, the example transmissionsignal generator 104 may generate any type of signal. The exampletransmission signal generator 104 transmits the generated signal to theexample transmission transducer 106 to transmit the generated signal(e.g., the transmitted signal 107).

The example transmission transducer 106 of FIG. 1 is an electricaldevice that outputs the generated signal from the example transmissionssignal generator 104. In some examples, the transmission transducer 106is an antenna (e.g., a beamform antenna) to transmit the signal (e.g.,light signal, radio signal, and/or sound signal) to the example target102. Alternatively, the transmission transducer 106 may be a diode (e.g.a light emitting diode, a laser diode, etc.) and/or any device capableof outputting light signals, radio signals, and/or sound signals.

The example receiver reference signal transducer 108 of FIG. 1 receivesthe transmitted signal 107 without being reflected off the exampletarget 102 and processes the transmitted signal 107 to generate areference signal. The received transmitted signal 107 may be differentfrom the signal generated by the example transmission signal generator104 due to front end system delay and/or other non-idealities. In someexamples, the transmitted signal 107 is the ideal signal generated bythe example transmission signal generator 104. The receiver referencesignal transducer 108 includes a low-pass filter to filter highfrequency noise of the transmitted signal 107. The example receiverreference signal transducer 108 may be an antenna (e.g., a beamformantenna) and/or a diode (e.g. photodiode) to receive the signal (e.g.,light signal, radio signal, and/or sound signal) from the exampletransmission transducer 106. Alternatively, the example receiverreference signal transducer 108 may be any device capable of receivinglight signals, radio signals, and/or sound signals.

The example receiver reflected signal transducer 110 of FIG. 1 receivesthe reflected signal 109 (e.g., the transmitted signal 107 after beingreflected off the example target 102). A received signal that isreflected off the example target 102 is herein defined as a reflectedsignal. Because the reflected signal 109 is not received directly fromthe example transmission transducer 106 (e.g., the transmitted signal107 is reflected off the example target 102 and travels a longdistance), the reflected signal 109 is subject to additional noise andmay have a smaller SNR ratio than the reference signal. The receiverreflected signal transducer 110 includes a low-pass filter to removehigh frequency noise from the reflected signal 109. In some examples,the example reflected signal transducer 110 is an antenna (e.g., abeamform antenna) and/or a diode (e.g. photodiode) to receive the signal(e.g., light signal, radio signal, and/or sound signal) from the exampletransmission transducer 106 after being reflected off the example target102. The example receiver reflected signal transducer 110 may be anydevice capable of receiving light signals, radio signals, and/or soundsignals. In some examples, the receiver reflected signal transducer 110includes a low-pass filter and a band limit filter to band limit thereference signal.

The example reference signal path AFE 112 and the example reflectedsignal path AFE 114 of FIG. 1 are devices that include analog to digitalconverters to convert the analog reflected signals 109 and referencesignals to digital samples. The digital samples of the low-passreference signal and the low-pass reflected signal are a digitalrepresentation of the analog signals. In some examples, the referencesignal AFE 112 and the reflected signal AFE 114 may be combined into oneAFE that includes one or more analog to digital converts to convert thereflected signals 109 and the reference signals to digital samples. Insome examples, components of the reference signal AFE 112 and/or thereflected signal AFE 114 may be shared between the reference signal pathand the reflected signal path. The digital samples of the low-passreference signal and the low-pass reflected signal are transmitted tothe example distance estimator 116 for further processing.

The example distance estimator 116 of FIG. 1 receives the low-passreference signal samples and the low-pass reflected signal samples andgenerates two correlations. The first correlation is a coarsecorrelation used to determine a coarse delay estimation corresponding toa distance of the example target 102 with high ambiguity and the secondcorrelation is a fine correlation used to determine a fine delayestimation corresponding to the distance of the example target 102 withlow ambiguity. As described above, the coarse estimation is used todetermine a time sample range and the fine estimation is used todetermine the optimal time sample within the time sample range. Theoptimal time corresponds to a distance of the example target 102. Asfurther described in conjunction with FIG. 2, the coarse correlation isa correlation of the low-pass reflected signal samples with the low-passreference signal samples. In some examples, the distance estimator 116windows (e.g., applies a Hamming window) the low-pass samples prior tothe coarse correlation to further reduce the ambiguity of the coarseestimation. Additionally, the low-pass reflected signal samples and thelow-pass reference signal samples are high-pass filtered to create aband-pass reflected signal and a band-pass reference signal. As furtherdescribed in conjunction with FIG. 2, the fine correlation is acorrelation of the band-pass reflected signal samples and the band-passreference signal samples. In some examples, the distance estimator 116outputs a distance estimate signal including the distance to the exampletarget 102 to a circuit and/or processor within and/or coupled to theexample ranging system 100.

FIG. 2 is a block diagram of an example implementation of the distanceestimator 116 of FIG. 1, disclosed herein, to determine the distance tothe example target 102 of FIG. 1. While the example distance estimator116 is described in conjunction with the example ranging system 100 andthe example target 102 of FIG. 1, the example distance estimator 116 maybe utilized to determine a distance to any target using any rangingsystem. The example distance estimator 116 includes example high-passfilters 200 a, 200 b, an example correlator(s) 204, an example delayestimator 206, example band-pass reflected signal samples 210, exampleband-pass reference signal samples 212, an example coarse correlation214, and an example fine correlation 216.

The example high-pass filter 200 a of FIG. 2 receives the low-passreference signal samples from the reference signal path AFE 112 of FIG.1 and the high-pass filter 200 b receives the low-pass reflected signalsamples from the reflected signal path AFE 114 of FIG. 1. The examplehigh-pass filters 200 a, 200 b filter the low-pass signals from thelow-pass reference signal samples and the low-pass reflected signalsamples. In some examples, the high-pass filters 200 a, 200 b may becombined in a single high-pass filter capable of filtering both thelow-pass reference signal and the low-pass reflected signal. High-passfiltering a low-pass signal generates a band-pass signal. Thus, theoutput of the example high-pass filters 200 a, 200 b is the exampleband-pass reflected signal samples 210 and the example band-passreference signal samples 212. The example band-pass reflected signalsamples 210 and the example band-pass reference signal samples 212 aretransmitted to the example correlator(s) 204.

The example correlator(s) 204 of FIG. 2 correlates (e.g., usingcross-correlation) the example band-pass reflected signal samples 210with the example band-pass reference signal samples 212 to generate theexample fine correlation 216. Additionally, the example correlator(s)204 correlate the low-pass reflected signal samples and the low-passreference signal samples to generate the example coarse correlation 214.The fine correlation identifies a similarity between the band-passreflected signal and the band-pass reference signal and the coarsecorrelation identifies a similarity between the low-pass reflectedsignal samples and the low pass reference signal samples. In someexamples, the example correlator(s) 204 cross correlates two signalsusing the following equation:[S ₁ *S ₂](t)=∫_(−∞) ^(+∞) S ₁*(τ)S ₂(τ+t)dτ  (Equation 1)where S₁* is the complex conjugate of S1.

As described above, the example correlations 214, 216 include a peakintensity value and side lobes values associated with a time sample. Thecoarse correlation 214 includes a wide, less pronounced peak and thefine correlation 216 includes a narrow, more pronounced peak. The peakof the coarse correlation 214 corresponds to a coarse delay estimateassociated with a distance to the example target 102 (FIG. 1) and thepeak of the fine correlation 216 corresponds to a fine delay estimateassociated with a distance to the example target 102. When noise ispresent in the reflected signal 109, the coarse delay estimate and thefine delay estimate may correspond to different time samplescorresponding to different distant estimations. In some examples, thecorrelator(s) 204 is two correlators, a first correlator to perform thecoarse correlation and a second correlator to perform the finecorrelation. The example correlator(s) 204 transmits the examplecorrelations (e.g. cross-correlations) 214, 216 to the example delayestimator 206.

The delay estimator 206 of FIG. 2 receives the example coarsecorrelation 214 and the example fine correlation 216 from the examplecorrelator(s) 204. The example delay estimator 206 determines the coarsedelay time sample based on the peak of the coarse correlation 214. Asdescribed above, because the coarse correlation 214 has a wider, lesspronounced peak in the presence of noise, the coarse delay time samplefor the coarse correlation 214 generates a less accurate estimate of thedistance to the example target 102 than the fine correlation 216. Thus,the example delay estimator 206 also determines an optimal delay timesample based on (A) the peak of the fine correlation 216 and (B) thecoarse delay time sample, as further described in conjunction with FIG.4. The optimal delay time sample corresponds to the time of flight ofthe transmitted reflected signal 109 and the time of flight correspondsto the distance of the example target 102 of FIG. 1. In some examples,the delay estimator 206 determines the distance of the example target102 based on the optimal time sample. The example delay estimator 206transmits the distance estimate signal which corresponds to the optimaltime sample, the time of flight, and/or the distance of the exampletarget 102.

FIG. 3 is a block diagram of an alternative example implementation ofthe distance estimator 116 of FIG. 1, disclosed herein, to determine thedistance to the example target 102 of FIG. 1. While the example distanceestimator 116 is described in conjunction with the example rangingsystem 100 and the example target 102 of FIG. 1, the example distanceestimator 116 may be utilized to determine a distance to any targetusing any ranging system. The example distance estimator 116 includesexample high-pass filters 200 a, 200 b, an example correlator(s) 204, anexample delay estimator 206, example band-pass reflected signal samples210, example band-pass reference signal samples 212, an example coarsecorrelation 214, and an example fine correlation 216 of FIG. 2. Theexample distance estimator 116 further includes an example windower(s)300, an example windowed reference signal 302, and an example windowedreflected signal 304 to further increase the accuracy of a coarsecorrelation.

The example windower(s) 300 of FIG. 3 receives the low-pass referencesignal samples from the example reference signal path AFE 112 and thelow-pass reflected signal samples from the example reflected signal pathAFE 114. The example windower(s) 300 windows the low-pass referencesignal samples and the low-pass reflected signal samples to generate theexample windowed reference signal 302 and the example windowed reflectedsignal 304. Windowing generates a rectangular window that is constantinside an interval of oscillation and zero everywhere else. Windowedsignals create a lower side lobe when the windowed signals 302, 304 arecorrelated. Additionally, windowing generates a wider coarse peak range,as further described in conjunction with FIG. 8A. In some examples, theexample windower(s) 300 includes two windowers, a first windower towindow the low-pass reference signal samples and a second windower towindow the low-pass reflected signal samples. The example windower(s)300 may window using a B-spline window function, a Welch windowfunction, a Hanning window function, and/or any other type of windowfunction. Alternatively, the low-pass reference signal samples and thelow-pass reflected signal samples may be transmitted directly to theexample correlator(s) 204 without being windowed by the examplewindower(s) 300 (e.g., the example windower(s) 300 may be removed fromthe example distance estimator 116).

While example manners of implementing the example distance estimator 116of FIG. 1 are illustrated in FIGS. 2 and/or 3, elements, processesand/or devices illustrated in FIGS. 2 and/or 3 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example high-pass filters 200 a, 200 b, the examplecorrelator(s) 204, the example delay estimator 206, the examplewindower(s) 300, and/or, more generally, the example distance estimator116 of FIGS. 2 and/or 3, may be implemented by hardware, machinereadable instructions, software, firmware and/or any combination ofhardware, machine readable instructions, software and/or firmware. Thus,for example, any of the example high-pass filters 200 a, 200 b, theexample correlator(s) 204, the example delay estimator 206, the examplewindower(s) 300, and/or, more generally, the example distance estimator116 of FIGS. 2 and/or 3, could be implemented by analog and/or digitalcircuit(s), logic circuit(s), programmable processor(s), applicationspecific integrated circuit(s) (ASIC(s)), programmable logic device(s)(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). In apurely software and/or firmware implementation, at least one of theexample high-pass filters 200 a, 200 b, the example correlator(s) 204,the example delay estimator 206, the example windower(s) 300, and/or,more generally, the example distance estimator 116 of FIGS. 2 and/or 3,is/are hereby expressly defined to include a tangible computer readablestorage device or storage disk such as a memory, a digital versatiledisk (DVD), a compact disk (CD), a BLU-RAY®, a trademark of SonyCorporation, disk, etc. storing the software and/or firmware. Furtherstill, the example distance estimator 116 of FIGS. 2 and/or 3 includeselements, processes and/or devices in addition to, or instead of, thoseillustrated in FIG. 4, and/or may include more than one of any or all ofthe illustrated elements, processes and devices.

A flowchart representative of example machine readable instructions forimplementing the example distance estimator 116 of FIGS. 2 and/or 3 isshown in FIG. 4. In the examples, the machine readable instructionscomprise a program for execution by a processor such as the processor912 shown in the example processor platform 900 discussed below inconnection with FIG. 9. The program may be embodied in machine readableinstructions stored on a tangible computer readable storage medium suchas a CD-ROM, a floppy disk, a hard drive, a digital versatile disk(DVD), a BLU-RAY®, a trademark of Sony Corporation, disk, or a memoryassociated with the processor 912, but the entire program and/or partsthereof could alternatively be executed by a device other than theprocessor 912 and/or embodied in firmware or dedicated hardware.Further, although the example program is described with reference to theflowchart illustrated in FIG. 4, many other methods of implementing theexample distance estimator 116 of FIGS. 2 and/or 3 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined.

As mentioned above, the example process of FIG. 4 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a tangible computer readable storage medium suchas a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example process of FIG. 4 may be implemented usingcoded instructions (e.g., computer and/or machine readable instructions)stored on a non-transitory computer and/or machine readable medium suchas a hard disk drive, a flash memory, a read-only memory, a compactdisk, a digital versatile disk, a cache, a random-access memory and/orany other storage device or storage disk in which information is storedfor any duration (e.g., for extended time periods, permanently, forbrief instances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term non-transitory computer readablemedium is expressly defined to include any type of computer readablestorage device and/or storage disk and to exclude propagating signalsand to exclude transmission media. As used herein, when the phrase “atleast” is used as the transition term in a preamble of a claim, it isopen-ended in the same manner as the term “comprising” is open ended.

FIG. 4 is an example flowchart 400 representative of example machinereadable instructions that may be executed by the example distanceestimator 116 of FIGS. 2 and/or 3 to determine the distance to theexample target 102 of FIG. 1. As previously described, the determinationof the distance is based on a duration of time between when a signal wastransmitted and when the reflected signal 109 is received. The exampledistance estimator 116 determines the duration of time based on anoptimal delay time sample corresponding to (A) a peak of the examplecoarse correlation 214 and (B) a peak of the example fine correlation216 of FIG. 2. The optimal time sample corresponds to the best estimateof the distance to the example target 102.

The example high-pass filters 200 a, 200 b high-pass filters thelow-pass reference signal samples from the example reference signal pathAFE 112 and the low-pass reflected signal samples from the examplereflected signal path AFE 114 to generate the example band-passreference signal samples 212 and the example band-pass reflected signalsamples 210 (block 402). An example of the band-pass reflected signalsamples 210 and the band-pass reference signal samples 212 is shown andfurther described in FIG. 7. The example windower 300 windows thelow-pass reference signal samples and the low-pass reflected signalsamples (block 404). As described above in conjunction with FIG. 3,windowing the low-pass reference signal samples and the low-passreflected signal samples causes the coarse interpolation to have alarger SNR, further decreasing the ambiguity of determining the peak ofthe coarse correlation. The windowed reference signal 302 and thewindowed reflected signal 304 are transmitted to the examplecorrelator(s) 204.

The example correlator(s) 204 correlates the band-pass reference signalsamples 212 with the band-pass reflected signal samples 210 to generatethe example fine correlation 216 (FIG. 2) (block 406). As describedabove in conjunction with FIG. 2, the correlator(s) 204 correlates theband-pass signals using Equation 1. An example of the result of the finecorrelation 216 is shown and further described in conjunction with FIG.8B. The example correlator(s) 204 transmits the example finecorrelations 216 (FIG. 2) to the example delay estimator 206.

The example correlator(s) 204 correlates the low-pass reference signalsamples with the low-pass reflected signal samples or correlates thewindowed reference signal 302 with the windowed reflected signal 304 togenerate the example coarse correlation 214 (FIG. 2) (block 408). Asdescribed above in conjunction with FIG. 2, the correlator(s) 204correlates the low-pass signals or the windowed signals usingEquation 1. An example of the result of the example coarse correlation214 is shown and further described in conjunction with FIG. 8A. Theexample correlator(s) 204 transmits the example coarse correlations 214(FIG. 2) to the example delay estimator 206.

The example delay estimator 206 determines a coarse delay estimationbased on the peak of the example coarse correlation 214 (block 410). Theexample delay estimator 206 determines the coarse delay time sampleestimation by determining which time sample corresponds to the peak ofthe example coarse correlation 214. As described above, the coarse delaytime sample estimation determines a time sample associated with adistance to the example target 102 (FIG. 1) that is accurate but notvery precise. To optimize the time sample estimate (e.g., make theaccurate estimate more precise), the example delay estimator 206determines an optimal time delay time sample estimation based on thecoarse delay estimation and the example fine correlation 216 (block412). In some examples, the example delay estimator 206 may determine anoptimal delay time sample based on the peak associated with the timesample closest to the coarse delay time sample estimate. For example, ifthe coarse delay sample estimate is associated with the 3,000th timesample and the closest peak of the fine delay correlation 216 isassociated with the 3,150th time sample, the example delay estimator 206determines the optimal delay time sample estimation to be associatedwith the 3,150th time sample. In this manner, the probability of cycleslip is minimized because the coarse correlation determines the delaytime sample range and the fine correlation determines the highly preciseoptimal delay time sample based on the delay sample range associatedwith the coarse correlation.

The example delay estimator 206 determines the distance to the exampletarget 102 based on the optimal delay time sample estimation (block414). As described above, each time sample corresponds to a distance ofthe example target 102. Thus, the optimal delay time sample estimationcorresponds to a time of flight of the reflected signal 109 and distanceof the target 102. In some examples, the example delay estimator 206determines the distance of the target 102 based on the optimal delaytime sample estimation directly. Alternatively, the example delayestimator 206 determines the distance of the target 102 by convertingthe optimal delay time sample estimation into a time value (e.g.nanoseconds) associated with the time of flight of the reflected signal109 and then uses the time of flight to calculate the distance to theexample target 102. The example delay estimator 206 outputs the distanceto the example target 102 in a distance estimate signal to a circuitand/or processor coupled to and/or within the example ranging system 100of FIG. 1 (block 416).

FIG. 5 illustrates example graphs 500, 502 illustrating the exampletransmitted signal 107 of FIG. 1 that is transmitted (e.g., via theexample transmission transducer 106 of FIG. 1) to the example target 102(FIG. 1) and the example reflected signal 109 of FIG. 1 that is receivedby the example receiver reflected signal transducer 110. The examplegraphs 500, 502 illustrate one repetition period of the transmittedpulse. The example transmitted signal 107 is a narrowband continuouswave modulated pulse at 80 Megahertz with a DC offset, an average powerof 75 milliwatts, and a peak power of 1181 milliwatts. Alternatively,the example transmitted signal 107 may be any type of signal oscillatingat any frequency with any amplitude. The example transmitted signal 107begins oscillating at 0 nanoseconds (ns) and continues to oscillatinguntil about 120 ns. The example reflected signal 109 is the exampletransmitted signal 107 after being reflected off the example target 102of FIG. 1. The example reflected signal 109 is a wave modulated pulse at80 Megahertz with a DC offset, an average power of 75 milliwatts, and apeak power of 1181 milliwatts. Alternatively, the example reflectedsignal 109 may be any type of signal oscillating at any frequency withany amplitude. The example reflected signal 109 begins oscillating at1000 ns and continues to oscillating until about 1120 ns (e.g., due tothe time of flight).

FIG. 6 includes example graphs 600, 602 illustrating the examplewindowed reference signal 302 and the example windowed reflected signal304 of FIG. 2. The example windowed signals 302, 304 represent thereference signal and the reflected signal 109 of FIG. 1 after beingwindowed by the example windower(s) 300 of FIG. 3. The example windowedreference signal 302 increases to around 1.15 milliwatts at around 10 nsand decreases back to 0 milliwatts at around 120 ns. The examplewindowed reflected signal 304 increases to around 1.15 milliwatts ataround 1020 ns and decreases back to 0 milliwatts at around 1140 ns.

FIG. 7 includes example graphs 700, 702 illustrating the exampleband-pass reflected signal samples 210 and the example band-passreference signal samples 212 of FIG. 2. The example band-pass reflectedsignal samples 210 represent the example reflected signal 109 of FIG. 1(e.g., the example transmitted signal 107 of FIG. 5 after beingreflected off the example target 102 of FIG. 1), received and low-passfiltered by the example receiver reflected signal transducer 110, andhigh-pass filtered by the example high-pass filter 200 b of FIG. 2. Theexample band-pass reflected signal samples 210 begin to oscillate ataround 1025 ns and terminates oscillation at around 1350 ns. The exampleband-pass reference signal samples 212 represent the example referencesignal (e.g., the transmitted signal 107 received and low-pass filteredby the example receiver reference signal transducer 108 to generate areference signal, and high-pass filtered by the example high-pass filter200 a of FIG. 2). The example band-pass reference signal samples 212begin to oscillate at around 20 ns and terminates oscillation at around200 ns.

FIGS. 8A and 8B illustrate an example coarse correlation graph 800including the example coarse correlation 214 and an example finecorrelation graph 810 including the example fine correlation 216 of FIG.2. The example coarse correlation graph 800 includes an example coarsepeak 802, example coarse side lobes 804 a, 804 b, an example coarse peakto side lobe range 806, and an example coarse peak range 808. Theexample fine correlation graph 810 includes an example fine peak 812,example fine side lobes 814 a, 814 b, an example fine peak to side loberange 816, and an example fine peak range 818.

The example coarse peak 802 of FIG. 8A is an intensity value of thecoarse correlation 214 with the highest intensity value (e.g., the mainlobe). As shown in the example coarse correlation graph 800, the examplepeak 802 has an intensity value of 1.0 at around the 3480th time sample.The 3480th time sample corresponds to the time of flight of thereflected signal 109 which may be used to calculate the distance to theexample target 102 of FIG. 1. The example coarse side lobes 804 a, 804 bcorrespond to the local maximum intensity values closest to the examplecoarse peak 802. As shown in the example coarse correlation graph 800,the coarse example side lobes 804 a, 804 b both have an intensity valueof 0.88 at around the 3440th sample and the 3525th sample, respectively.As previously described in conjunction with FIG. 2, the example coarsecorrelation 214 corresponds to a correlation of either (A) low-passreference signal samples with low-pass reflected signal samples or (B) awindowed reference signal 302 with a windowed reflected signal 304.Additionally, the example fine correlation 216 corresponds to acorrelation of the example band-pass reference signal samples 212 withthe example band-pass reflected signal samples 210 of FIG. 2.

The example coarse correlation 214 of FIG. 8A includes the examplecoarse peak to side lobe range 806 corresponding to a peak to side loberatio (e.g., the larger the peak to side lobe range, the higher the peakto side lobe ratio). The higher the peak to side lobe range and/or ratiothe more accurate the estimation of the distance to the example target102 is. As shown in the example coarse correlation graph 800, theexample coarse peak to side lobe range 806 is 0.12 (e.g., 1.0-0.88) andthe peak to side lobe ratio is 1.14 (e.g., 1.0/0.88). The example coarsecorrelation 214 includes the example coarse peak range 808 correspondingto the number of time samples between valleys corresponding to the peak.Noise can create spikes at any time sample. Thus, the wider the coarsepeak range 808 is, the less precise the estimate of the distance will bebecause noise can create an artificial peak at a point near the actualpeak. As shown in the example coarse correlation 214, the example coarsepeak range 808 is around 45 time samples.

The example peak 812 of FIG. 8B is an intensity value of the finecorrelation 216 with the highest intensity value (e.g., the main lobe).As shown in the example fine correlation graph 810, the example finepeak 812 has an intensity value of 1.0 at around the 3480th time sample.The 3480th time sample corresponds to the time of flight of thereflected signal 109 which may be used to calculate the distance to theexample target 102 of FIG. 1. The example fine side lobes 814 a, 814 bcorrespond to the local maximum intensity values closest to the examplefine peak 812. As shown in the example fine correlation graph 810, theexample fine side lobes 814 a, 814 b both have an intensity value of0.98 at around the 3440th sample and the 3525th sample, respectively.

The example fine correlation 216 of FIG. 8B includes the example finepeak to side lobe range 816 corresponding to a peak to side lobe ratio(e.g., the larger the peak to side lobe range, the higher the peak toside lobe ratio). The higher the peak to side lobe range and/or ratiothe more accurate the estimation of the distance to the example target102 is. As shown in the example fine correlation graph 810, the examplefine peak to side lobe range 816 is 0.02 (e.g., 1.0-0.98) and the peakto side lobe ratio is 1.02 (e.g., 1.0/0.98). Noise can create spikes atany time sample. Thus, the lower the peak to side lobe range and/orratio is, the higher the change that noise will increase the intensityvalue of one of the fine example side lobes 814 a, 814 b to an intensityhigher than the example fine peak 812 causing an increased probabilityof cycle slip. The example fine correlation 216 includes the examplefine peak range 818 corresponding to the number of time samples betweenvalleys corresponding to the peak. As shown in the example finecorrelation 216, the example fine peak range 818 is around 55 timesamples.

As shown in the example correlations 214, 216 of FIGS. 8A and 8B, theexample coarse correlation 214 is optimal for accuracy due to the largecoarse peak to side lobe range 806 and the example fine correlation 216is optimal for precision due to the small fine peak range 818.Additionally, because noise applied to the reflected signal 109associated with the example coarse correlation 214 can create spikes inthe example coarse correlation 214 and the coarse peak range 808 is solarge, a spike at the 3480th time sample may cause an intensity valuehigher than the actual coarse peak 802 at the 3480th time sample,leading to a less precise estimate. Likewise, because noise applied tothe reflected signal 109 associated with the example fine correlation216 can create spikes in the example fine correlation 216 and the finepeak to side lobe range 816 is so low, a spike at the 3440th time samplemay cause an intensity value higher than the actual fine peak 812 at the3440th time sample causing a cycle slip, leading to a less accurateestimate. The example distance estimator 116 of FIGS. 1 and 2 minimizesthe disadvantages and maximize the advantages of the example coarsecorrelation 214 and the example fine correlation 216 by using theprecision of the example fine correlation 216 to identify an optimaltime sample estimate based on the time sample associated with theaccurate coarse peak 802 of the example coarse correlation 214.

FIG. 9 is a block diagram of an example processor platform 900 capableof executing the instructions of FIG. 4 to implement the exampledistance estimator 116 of FIGS. 1, 2, and 3. The processor platform 900can be, for example, a server, a personal computer, a mobile device(e.g., a cell phone, a smart phone, a tablet such as an iPad™), apersonal digital assistant (PDA), an Internet appliance, or any othertype of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by integratedcircuits, logic circuits, microprocessors or controllers from anydesired family or manufacturer.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The example processor 912 of FIG. 9 executes theinstructions of FIG. 4 to implement the example high-pass filters 200 a,200 b the example correlator(s) 204, the example delay estimator 206,and the example windower(s) 300 of FIGS. 2 and/or 3 to implement theexample distance estimator 116. The processor 912 of the illustratedexample is in communication with a main memory including a volatilememory 914 that includes the local memory 913 and a non-volatile memory916 via a bus 918. The volatile memory 914 may be implemented bySynchronous Dynamic Random Access Memory (SDRAM), Dynamic Random AccessMemory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM) and/or anyother type of random access memory device. The non-volatile memory 916may be implemented by flash memory and/or any other desired type ofmemory device. Access to the main memory 914, 916 is controlled by aclock controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, a sensor, a microphone, a camera(still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, and/or speakers). The interface circuit 920 of theillustrated example, thus, typically includes a graphics driver card, agraphics driver chip or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, BLU-RAY®, a trademark of SonyCorporation, disk drives, RAID systems, and digital versatile disk (DVD)drives.

The coded instructions 932 of FIG. 4 may be stored in the mass storagedevice 928, in the volatile memory 914, in the non-volatile memory 916,and/or on a removable tangible computer readable storage medium such asa CD or DVD.

From the foregoing, it would be appreciated that the above disclosedmethod, apparatus, and articles of manufacture accurately determine adistance of a target by determining a time of flight for a reflectedsignal based on a coarse correlation of narrowband low-pass signals anda fine correlation of narrowband band-pass signals. Examples disclosedherein combine the accuracy of the coarse correlation with the precisionof the fine correlation to create a highly accurate and highly preciseestimate of the target. Examples disclosed herein reduce range errorstandard deviation by 18% which is equivalent to 20% reduction oftransmission power. Additionally, examples disclosed hereinsignificantly reduce the probability of cycle slip (e.g., mistakenlydetermining that a side lobe is a peak due to spikes associated withnoise).

Some conventional techniques only low-pass filter a reference signal anda reflected signal. However, such conventional techniques have lessprecise distance estimations due to noise around a peak of a correlationof the low-pass signals causing the peak to be used to make the distanceestimation. Other conventional techniques only band-pass filter areference signal and a reflected signal. However, such conventionaltechniques have an increased probability of cycle slip due to noisearound a side lobe of a correlation of the low-pass signals causing theside lobe to be used to make the distance estimation. Examples disclosedherein alleviate such problems associated with such conventionaltechniques by using a coarse correlation of the low-pass referencesignal and the low-pass reflected signal to determine an accurateestimation range and a band-pass fine correlation of the band-passreference signal and the reflected signal to determine a preciseestimation within the accurate estimation range.

Modifications are possible in the described embodiments, and otherembodiments are possible, within the scope of the claims.

What is claimed is:
 1. A method comprising: generating band-passreference signal samples using reference signal samples; generatingband-pass reflected signal samples using reflected signal samples;generating a first correlation based on the reference signal samples andthe reflected signal samples; correlating the band-pass reference signalsamples with the band-pass reflected signal samples to generate a secondcorrelation; determining a first time sample at which a first peak ofthe first correlation occurs; determining, based on the first timesample, a second time sample at which a second peak of the secondcorelation occurs; and determining a distance to a target based on thesecond time sample.
 2. The method of claim 1 wherein the secondcorrelation includes a plurality of peaks that includes the second peak,and the second peak occurs closest in time to the first peak.
 3. Themethod of claim 1, wherein the first peak has a highest intensity valueof the first correlation.
 4. The method of claim 1, wherein thereference signal samples include low-pass reference signal samples, andthe reflected signal samples include low-pass reflected signal samples.5. The method of claim 4, wherein: generating the band-pass filteredreference signal samples includes high-pass filtering the low-passreference signal samples; and generating the band-pass filteredreflected signal samples includes high-pass filtering the low-passreflected signal samples.
 6. The method of claim 4, wherein generatingthe first correlation includes: performing a windowing function on thelow-pass reference signal samples to generate windowed reference signalsamples; performing the windowing function on the low-pass reflectedsignal samples to generate windowed reflected signal samples; andcorrelating the windowed reference signal samples with the windowedreflected signal samples.
 7. The method of claim 1, wherein: thereference signal samples are generated from a narrowband signaltransmitted toward the target; and the reflected signal samples aregenerated from the narrowband signal after being reflected off thetarget.
 8. An apparatus comprising: receiver circuitry including alow-pass filter; analog front end circuitry coupled to the receivercircuitry, the analog front end circuitry including analog-to-digitalcircuitry; a processor coupled to the analog front end circuitry, theprocessor configured to: high-pass filter low-pass reference signalsamples to generate band-pass reference signal samples; high-pass filterlow-pass reflected signal samples to generate band-pass reflected signalsamples, the low-pass reference signal samples generated by theanalog-to-digital circuitry from a reference signal received andlow-pass filtered by the receiver, and the low-pass reflected signalsamples generated by the analog-to-digital circuitry from the referencesignal reflected off a target and then received and low-pass filtered bythe receiver; generate a first correlation based on the low-passreference signal samples and the low-pass reflected signal samples;correlate the band-pass reference signal samples with the band-passreflected signal samples to generate a second correlation; determine afirst time sample at which a first peak of the first correlation occurs,the first peak having a highest intensity value; determine, based on thefirst time sample, a second time sample at which a second peak of thesecond correlation occurs; and determine a distance to the target basedon the second time sample.
 9. The apparatus of claim 8, wherein theprocessor is further configured to select the second peak from aplurality of peaks of the second correlation, wherein the second peakoccurs closest in time to the first peak.
 10. The apparatus of claim 8,wherein the processor is further configured to: determine a time offlight based on the second time sample, and determine the distance tothe target based on the time of flight.
 11. The apparatus of claim 8,wherein the processor is further configured to: apply a windowingfunction to the low-pass reference signal samples to generate windowedreference signal samples; apply the windowing function to the low-passreflected signal samples to generates windowed reflected signal samples;and correlate the windowed reference signal samples with the windowedreflected signal samples to generate the first correlation.
 12. Theapparatus of claim 8, wherein the reference signal is a narrowbandsignal.
 13. A non-transitory computer readable medium storinginstructions that are executable by a processor to cause the processorto perform a method comprising: generating band-pass reference signalsamples using reference signal samples; generating band-pass reflectedsignal samples using reflected signal samples; generating a firstcorrelation based on the reference signal samples and the reflectedsignal samples; correlating the band-pass reference signal samples withthe band-pass reflected signal samples to generate a second correlation;determining a first time sample at which a first peak of the firstcorrelation occurs; determining, based on the first time sample, asecond time sample at which a second peak of the second correlationoccurs; and determining a distance to a target based on the second timesample.
 14. The non-transitory computer readable medium of claim 13,wherein the first peak has a highest intensity value.
 15. Thenon-transitory computer readable medium of claim 13, the method furthercomprising selecting the second peak from a plurality of peaks of thesecond correlation, wherein the second peak occurs closest in time tothe first peak.
 16. The non-transitory computer readable medium of claim13, wherein: the reference signal samples include low-pass referencesignal samples, and the reflected signal samples include low-passreflected signal samples; generating the band-pass filtered referencesignal samples includes high-pass filtering the low-pass referencesignal samples; and generating the band-pass filtered reflected signalsamples includes high-pass filtering the low-pass reflected signalsamples.
 17. The non-transitory computer readable medium of claim 16,wherein generating the first correlation includes: performing awindowing function on the low-pass reference signal samples to generatewindowed reference signal samples; performing the windowing function onthe low-pass reflected signal samples to generate windowed reflectedsignal samples; and correlating the windowed reference signal sampleswith the windowed reflected signal samples.
 18. The non-transitorycomputer readable medium of claim 13, wherein: the reference signalsamples are generated from a narrowband signal transmitted toward thetarget; and the reflected signal samples are generated from thenarrowband signal after being reflected off the target.